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What Are The Side Effects Of Metandienone?

**Answer – The "dual‑receptor" ligand used in the model**

In the breast‑epithelial cell model we introduce a **generic small‑molecule ligand that is capable of binding to both estrogen receptors (ERα/β) and the androgen receptor (AR)**.
A convenient chemically defined example of such a bifunctional compound is:

> **2‑(3,4‑dihydroxyphenyl)-1‑4‑methoxy‑benzyl‑propanol**
> (often abbreviated in the literature as "dual‑receptor ligand" or "DRL").

*Key properties of this ligand:*

| Property | Descripti> **Pitfalls & Tips**
> * **Incomplete dissolution** can lead to inaccurate concentration. Use mild heating (≤ 40 °C) and gentle vortexing.
> * **Precipitation upon cooling** may occur for some salts; verify solubility at storage temperature.
> * **pH drift** is uncomm> *db* is defined in `docker-compose.yml` with PostgreSQL.

### b) Migrate & seed

```bash
# Prisma example
npx prisma migrate dev --name init
npx prisma db seed
```

Or, for TypeORM:

```bash
npm run typeorm migration:run
```

### c) Start backend and frontend

```bash
# In one terminal
npm run start:dev # starts NestJS + Vite proxy

# In another terminal (optional)
cd client && npm run dev # if you need separate dev server
```

> The `start:dev` script runs the NestJS app with Hot Reload (`ts-node-dev`) and uses Vite as a middleware.
> All API calls are proxied to `/api/*` so no CORS issues.

#### 3. Production build

```bash
# Build frontend assets into dist/client (handled by vite in nestjs)
npm run build:client

# Build NestJS (tsc output in dist)
npm run build:nest

# Start production server
node dist/main.js
```

> The built applicati>
it('returns status 200', async () =>
c>= 7 |
| coverage | >= 6 |
| flake8 | >= 3.9 |
| pydocstyle | >= 5 |
| sphinx | >= 5 |

---

## 5. Implementation Notes

1. Ensure that the test suite covers all modules in the project.
2. Provide a `requirements.txt` or `pyproject.toml` with dependencies.
3. Use a `Makefile` or `invoke` tasks for convenience: `make test`, `make lint`.
4. Document the process in README so that contributors know how to run tests locally.

---

**End of Spec**

This spec can be used as a baseline. In a real scenario, you'd fill it with actual functions, classes, and tests.

Now, I'll write the answer: Provide an example spec and some sample test code? But the question might ask for "how to design a unit test spec for this repository." So answer should include explanation of typical structure, how to create spec file, naming conventions, etc. Also mention using `pytest` fixtures for setup/teardown.

The answer can be something like:

1. Understand domain: identify main modules.
2. For each module, write tests covering positive and negative scenarios.
3. Use test doubles (mocks) if necessary.
4. Write a spec file: describe behavior in plain English + code skeleton.
5. Provide example: `test_user_service.py` with class `TestUserService`.

Also mention guidelines for naming: `test_*.py`, functions starting with `test_`. Use `pytest.fixture` to set up environment.

Wrap up by summarizing approach and giving example of spec file.

Ok, let's produce the answer.## 1. Understand what you’re testing

Before you can write a **specification** (the "spec" that tells *what* your code should do) you need to know:

| Item | Why it matters |
|------|----------------|
| **Domain model** – What are the main objects? (`User`, `Order`, …) | You’ll be testing behaviour on these objects. |
| **Responsibilities** – Which class owns what logic? | Tests shouldn’t touch code that isn’t its responsibility. |
| **Public API** – The methods you expose (e.g., `order.add_item(item)` or `user.authenticate(password)`) | These are the only things your tests should call. |
| **Edge cases & invariants** – What constraints must always hold? (`price >= 0`, an order can’t be shipped twice) | Tests guard against future regressi

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